_base_ = [
    # '../_base_/models/fpn_r50.py',
    '../_base_/datasets/potsdam.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_80k.py'
]
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)

model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://resnest101',
    backbone=dict(
        type='ResNeSt',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 2, 4),
        strides=(1, 2, 2, 2),
        stem_channels=128,
        radix=2,
        reduction_factor=4,
        avg_down_stride=True,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=False,
        style='pytorch',
        contract_dilation=True),

    #neck=dict(type='Feature2Pyramid', embed_dim=768, rescales=[4, 2, 1, 0.5]),
         
    decode_head = dict(
        type='UPerHead',
        in_channels=[256, 512, 1024, 2048],
        in_index=[0, 1, 2, 3],
        pool_scales=(1, 2, 3, 6),
        channels=512,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=[
            dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=0.5),
            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=0.5)
        ]),
        
    auxiliary_head = dict(
        type='FCNHead',
        in_channels=1024,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=[
            dict(type='CrossEntropyLoss', loss_name='loss_ce', loss_weight=0.5),
            dict(type='DiceLoss', loss_name='loss_dice', loss_weight=0.5)
        ]),
# model training and testing settings
train_cfg = dict(),
test_cfg = dict(mode='whole'))